In 2022, Croatia reported 355.7 deaths from cancer per 100,000 population, the highest cancer mortality rate in Europe. Hungary followed with 332.8 cancer deaths per 100,000, and then Italy with 325.6 cancer deaths per 100,000 population. This statistic displays the mortality rate of cancer in Europe in 2022, by country (per 100,000 population).

This statistic displays the five-year survival rate in children with diagnosed cancer, by selected locations, time periods, and type of cancer. In Australia, children with leukaemias had a five-year chance of survival of over 80 percent in the measured period 1997-2006. In comparison, Chinese children with leukaemias in Shanghai had a chance of little more than 50 percent to survive five years (measured in the period 2002-2005).

Cancer Mortality & Incidence Rates: (Country LVL)

Investigating Cancer Trends over time

By Data Exercises [source]

About this dataset

This dataset is a comprehensive collection of data from county-level cancer mortality and incidence rates in the United States between 2000-2014. This data provides an unprecedented level of detail into cancer cases, deaths, and trends at a local level. The included columns include County, FIPS, age-adjusted death rate, average death rate per year, recent trend (2) in death rates, recent 5-year trend (2) in death rates and average annual count for each county. This dataset can be used to provide deep insight into the patterns and effects of cancer on communities as well as help inform policy decisions related to mitigating risk factors or increasing preventive measures such as screenings. With this comprehensive set of records from across the United States over 15 years, you will be able to make informed decisions regarding individual patient care or policy development within your own community!

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How to use the dataset

This dataset provides comprehensive US county-level cancer mortality and incidence rates from 2000 to 2014. It includes the mortality and incidence rate for each county, as well as whether the county met the objective of 45.5 deaths per 100,000 people. It also provides information on recent trends in death rates and average annual counts of cases over the five year period studied.

This dataset can be extremely useful to researchers looking to study trends in cancer death rates across counties. By using this data, researchers will be able to gain valuable insight into how different counties are performing in terms of providing treatment and prevention services for cancer patients and whether preventative measures and healthcare access are having an effect on reducing cancer mortality rates over time. This data can also be used to inform policy makers about counties needing more target prevention efforts or additional resources for providing better healthcare access within at risk communities.

When using this dataset, it is important to pay close attention to any qualitative columns such as “Recent Trend” or “Recent 5-Year Trend (2)” that may provide insights into long term changes that may not be readily apparent when using quantitative variables such as age-adjusted death rate or average deaths per year over shorter periods of time like one year or five years respectively. Additionally, when studying differences between different counties it is important to take note of any standard FIPS code differences that may indicate that data was collected by a different source with a difference methodology than what was used in other areas studied

Research Ideas

• Using this dataset, we can identify patterns in cancer mortality and incidence rates that are statistically significant to create treatment regimens or preventive measures specifically targeting those areas.
• This data can be useful for policymakers to target areas with elevated cancer mortality and incidence rates so they can allocate financial resources to these areas more efficiently.
• This dataset can be used to investigate which factors (such as pollution levels, access to medical care, genetic make up) may have an influence on the cancer mortality and incidence rates in different US counties

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: death .csv | Column name | Description | |:-------------------------------------------|:-------------------------------------------------------------------...

In 2022, the mortality rate of breast cancer in women in Europe was **** per 100,000 women. Cyprus had the highest mortality rate at **** per 100,000, followed by Slovakia with **** per 100,000 women. Conversely, Spain had the lowest mortality rate at **** per 100,000. This statistic depicts the mortality rate of breast cancer in Europe in 2022 in women population, by country.

BackgroundThe 5-year survival rate of cancer patients is the most commonly used statistic to reflect improvements in the war against cancer. This idea, however, was refuted based on an analysis showing that changes in 5-year survival over time bear no relationship with changes in cancer mortality.MethodsHere we show that progress in the fight against cancer can be evaluated by analyzing the association between 5-year survival rates and mortality rates normalized by the incidence (mortality over incidence, MOI). Changes in mortality rates are caused by improved clinical management as well as changing incidence rates, and since the latter can mask the effects of the former, it can also mask the correlation between survival and mortality rates. However, MOI is a more robust quantity and reflects improvements in cancer outcomes by overcoming the masking effect of changing incidence rates. Using population-based statistics for the US and the European Nordic countries, we determined the association of changes in 5-year survival rates and MOI.ResultsWe observed a strong correlation between changes in 5-year survival rates of cancer patients and changes in the MOI for all the countries tested. This finding demonstrates that there is no reason to assume that the improvements in 5-year survival rates are artificial. We obtained consistent results when examining the subset of cancer types whose incidence did not increase, suggesting that over-diagnosis does not obscure the results.ConclusionsWe have demonstrated, via the negative correlation between changes in 5-year survival rates and changes in MOI, that increases in 5-year survival rates reflect real improvements over time made in the clinical management of cancer. Furthermore, we found that increases in 5-year survival rates are not predominantly artificial byproducts of lead-time bias, as implied in the literature. The survival measure alone can therefore be used for a rough approximation of the amount of progress in the clinical management of cancer, but should ideally be used with other measures.

Dataset Description This dataset contains information on cancer deaths by country, type, and year. It includes data on 18 different types of cancer, including liver cancer, kidney cancer, larynx cancer, breast cancer, thyroid cancer, stomach cancer, bladder cancer, uterine cancer, ovarian cancer, cervical cancer, prostate cancer, pancreatic cancer, esophageal cancer, testicular cancer, nasopharynx cancer, other pharynx cancer, colon and rectum cancer, non-melanoma skin cancer, lip and oral cavity cancer, brain and nervous system cancer, tracheal, bronchus, and lung cancer, gallbladder and biliary tract cancer, malignant skin melanoma, leukemia, Hodgkin lymphoma, multiple myeloma, and other cancers.

Data Fields The dataset includes the following data fields:

• Country: The country where the cancer death occurred.
• Code: The country code for the country where the cancer death occurred.
• Year: The year in which the cancer death occurred.
• Liver cancer: The number of cancer deaths from liver cancer in the country in the year.
• Kidney cancer: The number of cancer deaths from kidney cancer in the country in the year.
• Larynx cancer: The number of cancer deaths from larynx cancer in the country in the year.
• Breast cancer: The number of cancer deaths from breast cancer in the country in the year.
• Thyroid cancer: The number of cancer deaths from thyroid cancer in the country in the year.
• Stomach cancer: The number of cancer deaths from stomach cancer in the country in the year.
• Bladder cancer: The number of cancer deaths from bladder cancer in the country in the year.
• Uterine cancer: The number of cancer deaths from uterine cancer in the country in the year.
• Ovarian cancer: The number of cancer deaths from ovarian cancer in the country in the year.
• Cervical cancer: The number of cancer deaths from cervical cancer in the country in the year.
• Prostate cancer: The number of cancer deaths from prostate cancer in the country in the year.
• Pancreatic cancer: The number of cancer deaths from pancreatic cancer in the country in the year.
• Esophageal cancer: The number of cancer deaths from esophageal cancer in the country in the year.
• Testicular cancer: The number of cancer deaths from testicular cancer in the country in the year.
• Nasopharynx cancer: The number of cancer deaths from nasopharynx cancer in the country in the year.
• Other pharynx cancer: The number of cancer deaths from other pharynx cancer in the country in the year.
• Colon and rectum cancer: The number of cancer deaths from colon and rectum cancer in the country in the year.
• Non-melanoma skin cancer: The number of cancer deaths from non-melanoma skin cancer in the country in the year.
• Lip and oral cavity cancer: The number of cancer deaths from lip and oral cavity cancer in the country in the year.
• Brain and nervous system cancer: The number of cancer deaths from brain and nervous system cancer in the country in the year.
• Tracheal, bronchus, and lung cancer: The number of cancer deaths from tracheal, bronchus, and lung cancer in the country in the year.
• Gallbladder and biliary tract cancer: The number of cancer deaths from gallbladder and biliary tract cancer in the country in the year.
• Malignant skin melanoma: The number of cancer deaths from malignant skin melanoma in the country in the year.
• Leukemia: The number of cancer deaths from leukemia in the country in the year.
• Hodgkin lymphoma: The number of cancer deaths from Hodgkin lymphoma in the country in the year.
• Multiple myeloma: The number of cancer deaths from multiple myeloma in the country in the year.
• Other cancers: The number of cancer deaths from other cancers in the country in the year.

Data Source The data in this dataset was collected from the World Health Organization (WHO). The WHO collects data on cancer deaths from countries around the world.

Usage This dataset can be used to study cancer deaths by country, type, and year. It can also be used to compare cancer death rates between different countries or over time.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16169071%2F98f6c6f321aad496b703685519b6df6a%2Fcancer-cells-th.jpg?generation=1694610742970317&alt=media" alt="">

Exploring County-Level Correlations in Cancer Rates and Trends

A Multivariate Ordinary Least Squares Regression Model

By Noah Rippner [source]

About this dataset

This dataset offers a unique opportunity to examine the pattern and trends of county-level cancer rates in the United States at the individual county level. Using data from cancer.gov and the US Census American Community Survey, this dataset allows us to gain insight into how age-adjusted death rate, average deaths per year, and recent trends vary between counties – along with other key metrics like average annual counts, met objectives of 45.5?, recent trends (2) in death rates, etc., captured within our deep multi-dimensional dataset. We are able to build linear regression models based on our data to determine correlations between variables that can help us better understand cancers prevalence levels across different counties over time - making it easier to target health initiatives and resources accurately when necessary or desired

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How to use the dataset

This kaggle dataset provides county-level datasets from the US Census American Community Survey and cancer.gov for exploring correlations between county-level cancer rates, trends, and mortality statistics. This dataset contains records from all U.S counties concerning the age-adjusted death rate, average deaths per year, recent trend (2) in death rates, average annual count of cases detected within 5 years, and whether or not an objective of 45.5 (1) was met in the county associated with each row in the table.

To use this dataset to its fullest potential you need to understand how to perform simple descriptive analytics which includes calculating summary statistics such as mean, median or other numerical values; summarizing categorical variables using frequency tables; creating data visualizations such as charts and histograms; applying linear regression or other machine learning techniques such as support vector machines (SVMs), random forests or neural networks etc.; differentiating between supervised vs unsupervised learning techniques etc.; reviewing diagnostics tests to evaluate your models; interpreting your findings; hypothesizing possible reasons and patterns discovered during exploration made through data visualizations ; Communicating and conveying results found via effective presentation slides/documents etc.. Having this understanding will enable you apply different methods of analysis on this data set accurately ad effectively.

Once these concepts are understood you are ready start exploring this data set by first importing it into your visualization software either tableau public/ desktop version/Qlikview / SAS Analytical suite/Python notebooks for building predictive models by loading specified packages based on usage like Scikit Learn if Python is used among others depending on what tool is used . Secondly a brief description of the entire table's column structure has been provided above . Statistical operations can be carried out with simple queries after proper knowledge of basic SQL commands is attained just like queries using sub sets can also be performed with good command over selecting columns while specifying conditions applicable along with sorting operations being done based on specific attributes as required leading up towards writing python codes needed when parsing specific portion of data desired grouping / aggregating different categories before performing any kind of predictions / models can also activated create post joining few tables possible , when ever necessary once again varying across tools being used Thereby diving deep into analyzing available features determined randomly thus creating correlation matrices figures showing distribution relationships using correlation & covariance matrixes , thus making evaluations deducing informative facts since revealing trends identified through corresponding scatter plots from a given metric gathered from appropriate fields!

Research Ideas

• Building a predictive cancer incidence model based on county-level demographic data to identify high-risk areas and target public health interventions.
• Analyzing correlations between age-adjusted death rate, average annual count, and recent trends in order to develop more effective policy initiatives for cancer prevention and healthcare access.
• Utilizing the dataset to construct a machine learning algorithm that can predict county-level mortality rates based on socio-economic factors such as poverty levels and educational attainment rates

Acknowledgements

If you use this dataset i...

In 2022, the mortality rate of prostate cancer in Europe was **** per 100,000. Estonia had the highest mortality rate at **** per 100,000, followed by Latvia with **** per 100,000 men. Conversely, Italy had the lowest mortality rate at **** per 100,000. This statistic depicts the mortality rate of prostate cancer Europe in 2022, by country.

In 2010, cancer deaths accounted for more than 15% of all deaths worldwide, and this fraction is estimated to rise in the coming years. Increased cancer mortality has been observed in immigrant populations, but a comprehensive analysis by country of birth has not been conducted. We followed all individuals living in Sweden between 1961 and 2009 (7,109,327 men and 6,958,714 women), and calculated crude cancer mortality rates and age-standardized rates (ASRs) using the world population for standardization. We observed a downward trend in all-site ASRs over the past two decades in men regardless of country of birth but no such trend was found in women. All-site cancer mortality increased with decreasing levels of education regardless of sex and country of birth (p for trend <0.001). We also compared cancer mortality rates among foreign-born (13.9%) and Sweden-born (86.1%) individuals and determined the effect of education level and sex estimated by mortality rate ratios (MRRs) using multivariable Poisson regression. All-site cancer mortality was slightly higher among foreign-born than Sweden-born men (MRR = 1.05, 95% confidence interval 1.04–1.07), but similar mortality risks was found among foreign-born and Sweden-born women. Men born in Angola, Laos, and Cambodia had the highest cancer mortality risk. Women born in all countries except Iceland, Denmark, and Mexico had a similar or smaller risk than women born in Sweden. Cancer-specific mortality analysis showed an increased risk for cervical and lung cancer in both sexes but a decreased risk for colon, breast, and prostate cancer mortality among foreign-born compared with Sweden-born individuals. Further studies are required to fully understand the causes of the observed inequalities in mortality across levels of education and countries of birth.

This dataset contains data about lung cancer Mortality. This database is a comprehensive collection of patient information, specifically focused on individuals diagnosed with cancer. It is designed to facilitate the analysis of various factors that may influence cancer prognosis and treatment outcomes. The database includes a range of demographic, medical, and treatment-related variables, capturing essential details about each patient's condition and history.

Key components of the database include:

Demographic Information: Basic details about the patients such as age, gender, and country of residence. This helps in understanding the distribution of cancer cases across different populations and regions.

Medical History: Information about each patient’s medical background, including family history of cancer, smoking status, Body Mass Index (BMI), cholesterol levels, and the presence of other health conditions such as hypertension, asthma, cirrhosis, and other cancers. This section is crucial for identifying potential risk factors and comorbidities.

Cancer Diagnosis: Detailed data about the cancer diagnosis itself, including the date of diagnosis and the stage of cancer at the time of diagnosis. This helps in tracking the progression and severity of the disease.

Treatment Details: Information regarding the type of treatment each patient received, the end date of the treatment, and the outcome (whether the patient survived or not). This is essential for evaluating the effectiveness of different treatment approaches.

The structure of the database allows for in-depth analysis and research, making it possible to identify patterns, correlations, and potential causal relationships between various factors and cancer outcomes. It is a valuable resource for medical researchers, epidemiologists, and healthcare providers aiming to improve cancer treatment and patient care.

id: A unique identifier for each patient in the dataset. age: The age of the patient at the time of diagnosis. gender: The gender of the patient (e.g., male, female). country: The country or region where the patient resides. diagnosis_date: The date on which the patient was diagnosed with lung cancer. cancer_stage: The stage of lung cancer at the time of diagnosis (e.g., Stage I, Stage II, Stage III, Stage IV). family_history: Indicates whether there is a family history of cancer (e.g., yes, no). smoking_status: The smoking status of the patient (e.g., current smoker, former smoker, never smoked, passive smoker). bmi: The Body Mass Index of the patient at the time of diagnosis. cholesterol_level: The cholesterol level of the patient (value). hypertension: Indicates whether the patient has hypertension (high blood pressure) (e.g., yes, no). asthma: Indicates whether the patient has asthma (e.g., yes, no). cirrhosis: Indicates whether the patient has cirrhosis of the liver (e.g., yes, no). other_cancer: Indicates whether the patient has had any other type of cancer in addition to the primary diagnosis (e.g., yes, no). treatment_type: The type of treatment the patient received (e.g., surgery, chemotherapy, radiation, combined). end_treatment_date: The date on which the patient completed their cancer treatment or died. survived: Indicates whether the patient survived (e.g., yes, no).

This dataset contains artificially generated data with as close a representation of reality as possible. This data is free to use without any licence required.

Good luck Gakusei!

Users can access data about cancer statistics, specifically incidence and mortality worldwide for the 27 major types of cancer. Background Cancer Mondial is maintained by the Section of Cancer Information (CIN) of International Agency for Research on Cancer by the World Health Organization. Users can access CIN databases including GLOBOCAN, CI5(Cancer Incidence in Five Continents), WHO, ACCIS(Automated Childhood Cancer Information System), ECO (European Cancer Observatory), NORDCAN and Survcan. User functionality Users can access a variety of databases. CIN Databases: GLOBOCAN provides acces s to the most recent estimates (for 2008) of the incidence of 27 major cancers and mortality from 27 major cancers worldwide. CI5 (Cancer Incidence in Five Continents) provides access to detailed information on the incidence of cancer recorded by cancer registries (regional or national) worldwide. WHO presents long time series of selected cancer mortality recorded in selected countries of the world. Collaborative projects: ACCIS (Automated Childhood Cancer Information System) provides access to data on cancer incidence and survival of children collected by European cancer registries. ECO (European Cancer Observatory) provides access to the estimates (for 2008) of the incidence of, and mortality f rom 25 major cancers in the countries of the European Union (EU-27). NORDCAN presents up-to-date long time series of cancer incidence, mortality, prevalence and survival from 40 cancers recorded by the Nordic countries. SurvCan presents cancer survival data from cancer registries in low and middle income regions of the world. Data Notes Data is available in different formats depending on which type of data is accessed. Some data is available in table, PDF, and html formats. Detailed information about the data is available.

BackgroundThe assessment of cancer survival is crucial for evaluating advancements in cancer management. As part of the nationwide HUN-CANCER EPI study, we examined the net survival of the Hungarian cancer patient population in 2011–2019.MethodsUsing extracted data from the Hungarian National Health Insurance Fund (NHIF) database, the HUN-CANCER EPI study aimed to assess net survival probabilities for various cancer types over the past decade by the Pohar Perme Estimator method, providing insights for sex and age-specific differences and enabling comparative analysis with other European countries.ResultsBetween 2011 and 2019, 526,381 newly diagnosed cancer cases were identified, with colorectal, lung, breast, prostate, and bladder cancers being the most common. Age-standardized 5-year net survival rates showed significant improvements from 2011-12 till 2017-19 periods for colorectal cancer from 55.08% to 59.78% (4.70%), lung cancer from 20.10% to 23.55% (3.45%), liver cancer from 11.21% to 16.97% (5.76%) and melanoma from 90.06% to 93.80% (3.73%), while clinically relevant, but not significant improvements for breast cancer from 85.03% to 86.84% (1.81%), prostate cancer from 88.13% to 89.76% (1.63%) and thyroid cancer from 87.23% to 92.36% (5.12%). Women generally had better survival probabilities, with notable variations across cancer types. We found no significant age-related differences in cancer survival in women, while survival improvements of colorectal cancer were more pronounced in younger cohorts among male patients. International comparisons using different mortality life tables demonstrated favorable breast and prostate cancer survival rates in Hungary compared to other Central Eastern European countries.ConclusionThe HUN-CANCER EPI study revealed positive trends in cancer survival for most cancer types between 2011 and 2019. The study highlights the continued positive trajectory of cancer survival in Hungary like to more developed European countries.

This dataset contains real-world information about colorectal cancer cases from different countries. It includes patient demographics, lifestyle risks, medical history, cancer stage, treatment types, survival chances, and healthcare costs. The dataset follows global trends in colorectal cancer incidence, mortality, and prevention.

Use this dataset to build models for cancer prediction, survival analysis, healthcare cost estimation, and disease risk factors.

Dataset Structure Each row represents an individual case, and the columns include:

Patient_ID (Unique identifier) Country (Based on incidence distribution) Age (Following colorectal cancer age trends) Gender (M/F, considering men have 30-40% higher risk) Cancer_Stage (Localized, Regional, Metastatic) Tumor_Size_mm (Randomized within medical limits) Family_History (Yes/No) Smoking_History (Yes/No) Alcohol_Consumption (Yes/No) Obesity_BMI (Normal/Overweight/Obese) Diet_Risk (Low/Moderate/High) Physical_Activity (Low/Moderate/High) Diabetes (Yes/No) Inflammatory_Bowel_Disease (Yes/No) Genetic_Mutation (Yes/No) Screening_History (Regular/Irregular/Never) Early_Detection (Yes/No) Treatment_Type (Surgery/Chemotherapy/Radiotherapy/Combination) Survival_5_years (Yes/No) Mortality (Yes/No) Healthcare_Costs (Country-dependent, $25K-$100K+) Incidence_Rate_per_100K (Country-level prevalence) Mortality_Rate_per_100K (Country-level mortality) Urban_or_Rural (Urban/Rural) Economic_Classification (Developed/Developing) Healthcare_Access (Low/Moderate/High) Insurance_Status (Insured/Uninsured) Survival_Prediction (Yes/No, based on factors)

Despite substantial improvements in survival from childhood cancer during the last decades, there are indications that survival rates for several cancer types are no longer improving. Moreover, evidence accumulates suggesting that socioeconomic and sociodemographic factors may have an impact on survival also in high-income countries. The aim of this review is to summarize the findings from studies on social factors and survival in childhood cancer. Several types of cancer and social factors are included in order to shed light on potential mechanisms and identify particularly affected groups. A literature search conducted in PubMed identified 333 articles published from December 2012 until June 2018, of which 24 fulfilled the inclusion criteria. The findings are diverse; some studies found no associations but several indicated a social gradient with higher mortality among children from families of lower socioeconomic status (SES). There were no clear suggestions of particularly vulnerable subgroups, but hematological malignancies were most commonly investigated. A wide range of social factors have been examined and seem to be of different importance and varying between studies. However, potential underlying mechanisms linking a specific social factor to childhood cancer survival was seldom described. This review provides some support for a relationship between lower parental SES and worse survival after childhood cancer, which is a finding that needs further attention. Studies investigating predefined hypotheses involving specific social factors within homogenous cancer types are lacking and would increase the understanding of mechanisms involved, and allow targeted interventions to reduce health inequalities.

Blood cancer survival rates vary widely across the globe — and the contrast between India and developed nations is both eye-opening and urgent. While countries like the U.S., UK, and Germany report survival rates of 60–75% for common blood cancers such as leukaemia, lymphoma, and myeloma, India’s figures remain significantly lower, often ranging between 30 and 40%.This gap is driven by several critical factors: late diagnosis, limited access to advanced treatments, lack of awareness, and uneven healthcare infrastructure. Yet, with timely detection and modern therapies like CAR T-cell therapy and bone marrow transplant, these numbers can improve — and lives can be saved.At bmtnext.com, BMT NEXT is working to close this survival gap by offering world-class care, cutting-edge treatment options, and personalised support for every patient. Our mission is to ensure that patients in India receive the same level of care and hope as those in the most advanced healthcare systems.It’s time to bridge the divide — and BMT NEXT is leading the way.

In 2022, the mortality rate of colorectal cancer in Europe was, among men, **** per 100,000, while among women it stood at **** per 100,000. For men, Croatia had the highest mortality rate at **** per 100,000, while Luxembourg had the lowest at **** per 100,000. For women, Croatia also had the highest mortality rate at **** per 100,000, while Austria had the lowest at **** per 100,000. This statistic depicts the mortality rate of colorectal cancer in Europe in 2022, by country and gender.

Despite structural and cultural similarities across the Nordic countries, differences in cancer survival remain. With a focus on similarities and differences between the Nordic countries, we investigated the association between socioeconomic position (SEP) and stage at diagnosis, anticancer treatment and cancer survival to describe patterns, explore underlying mechanisms and identify knowledge gaps in the Nordic countries We conducted a systematic review of population based observational studies. A systematic search in PubMed, EMBASE and Medline up till May 2021 was performed, and titles, abstracts and full texts were screened for eligibility by two investigators independently. We extracted estimates of the association between SEP defined as education or income and cancer stage at diagnosis, received anticancer treatment or survival for adult patients with cancer in the Nordic countries. Further, we extracted information on study characteristics, confounding variables, cancer type and results in the available measurements with corresponding confidence intervals (CI) and/or p-values. Results were synthesized in forest plots. From the systematic literature search, we retrieved 3629 studies, which were screened for eligibility, and could include 98 studies for data extraction. Results showed a clear pattern across the Nordic countries of socioeconomic inequality in terms of advanced stage at diagnosis, less favorable treatment and lower cause-specific and overall survival among people with lower SEP, regardless of whether SEP was measured as education or income. Despite gaps in the literature, the consistency in results across cancer types, countries and cancer outcomes shows a clear pattern of systematic socioeconomic inequality in cancer stage, treatment and survival in the Nordic countries. Stage and anticancer treatment explain some, but not all of the observed inequality in overall and cause-specific survival. The need for further studies describing this association may therefore be limited, warranting next step research into interventions to reduce inequality in cancer outcomes. Prospero protocol no: CRD42020166296

Objective: Lung cancer is one of the most common cancers worldwide and its survival is still poor. The objective of our study was to estimate long-term survival of Hungarian lung cancer patients at first time based on a nationwide review of the National Health Insurance Fund database.Methods: Our retrospective, longitudinal study included patients aged ≥20 years who were diagnosed with lung cancer (ICD-10 C34) between January 1, 2011 and December 31, 2016. Survival rates were evaluated by year of diagnosis, patient gender and age, and morphology of lung cancer.Results: 41,854 newly diagnosed lung cancer patients were recorded. Mean age at diagnosis varied between 64.7 and 65.9 years during study period. One- and 5-year overall survival rates for the total population were 42.2 and 17.9%, respectively. Survival was statistically associated with gender, age and type of lung cancer. Female patients (n = 16,362) had 23% better survival (HR: 0.77, 95% confidence interval (CI): 0.75–0.79; p < 0.001) than males (n = 25,492). The highest survival rates were found in the 20–49 age cohort (5Y = 31.3%) and if the cancer type was adenocarcinoma (5Y = 20.5%). We measured 5.3% improvement (9.2% adjusted) in lung cancer survival comparing the period 2015–2016 to 2011–2012 (HR: 0.95 95% CI: 0.92–0.97; p = 0.003), the highest at females <60 year (0.86 (adjusted HR was 0.79), interaction analysis was significant for age and histology types.Conclusion: Our study provided long-term Lung cancer survival data in Hungary for the first time. We found a 5.3% improvement in 5-year survival in 4 years. Women and young patients had better survival. Survival rates were comparable to–and at the higher end of–rates registered in other East-Central European countries (7.7%–15.7%).

📄 Dataset Description: This dataset contains global cancer patient data reported from 2015 to 2024, designed to simulate the key factors influencing cancer diagnosis, treatment, and survival. It includes a variety of features that are commonly studied in the medical field, such as age, gender, cancer type, environmental factors, and lifestyle behaviors. The dataset is perfect for:

Exploratory Data Analysis (EDA)

Multiple Linear Regression and other modeling tasks

Feature Selection and Correlation Analysis

Predictive Modeling for cancer severity, treatment cost, and survival prediction

Data Visualization and creating insightful graphs

Key Features: Age: Patient's age (20-90 years)

Gender: Male, Female, or Other

Country/Region: Country or region of the patient

Cancer Type: Various types of cancer (e.g., Breast, Lung, Colon)

Cancer Stage: Stage 0 to Stage IV

Risk Factors: Includes genetic risk, air pollution, alcohol use, smoking, obesity, etc.

Treatment Cost: Estimated cost of cancer treatment (in USD)

Survival Years: Years survived since diagnosis

Severity Score: A composite score representing cancer severity

This dataset provides a broad view of global cancer trends, making it an ideal resource for those learning data science, machine learning, and statistical analysis in healthcare.

In 2022, the highest cancer rate for men and women among European countries was in Denmark with 728.5 cancer cases per 100,000 population. Ireland and the Netherlands followed, with 641.6 and 641.4 people diagnosed with cancer per 100,000 population, respectively.
Lung cancer Lung cancer is the deadliest type of cancer worldwide, and in Europe, Germany was the country with the highest number of lung cancer deaths in 2022, with 47.7 thousand deaths. However, when looking at the incidence rate of lung cancer, Hungary had the highest for both males and females, with 138.4 and 72.3 cases per 100,000 population, respectively.
Breast cancer Breast cancer is the most common type of cancer among women with an incidence rate of 83.3 cases per 100,000 population in Europe in 2022. Cyprus was the country with the highest incidence of breast cancer, followed by Belgium and France. The mortality rate due to breast cancer was 34.8 deaths per 100,000 population across Europe, and Cyprus was again the country with the highest figure.